UP3.4

Land-air interaction occurs at the ground surface at a wide range of time scales in the form of mass, momentum, and energy exchange. As a result of the water and heat fluxes, the land acts as a water and mostly energy storehouse for the climate system. Last estimates based on multimodel comparisons quantify the land contribution to terrestrial energy budget at about 2 % in the last four decades, whilst other studies based on borehole temperature profiles (BTPs) scale it up to be a 6 %. This uncertainty makes it necessary to explore other data sources to determine the land energy uptake, mostly under the increasing energy imbalance due to the ongoing anthropogenic-induced climate change.

State-of-the-art land surface models (LSMs) resolve a subsurface whose bottom boundary condition placement (BBCP) is not deep enough to correctly represent its thermal structure. This results both in a constrained capability to store energy, and an overestimation of temperature variability and industrial trends with increasing depth. A 2000-year-long forced simulation using a version of the Max Planck Institute (MPI) Earth System Model (ESM), MPI-ESM, including a very deep version of the LSM (BBCP at 1417 m), allows for assessing the behavior of subsurface temperatures and heat storage at long term scales, with a particular focus on the land response to the last century global warming. The analysis also allows for extending the assessment to CMIP6 historical simulations and climate reanalysis data.

Preliminary results show the energy uptaken by the MPI-ESM simulation with a deep version of the LSM is well above the range of values provided by CMIP6 model-based estimates and much closer to the observations. This underlines the great importance of BBCP depth in correctly representing the role of the land component in the terrestrial energy budget.

How to cite: García Pereira, F., González Rouco, J. F., Melo Aguilar, C., Steinert, N., García Bustamante, E., de Vrese, P., Jungclaus, J., Lorenz, S., and Hagemann, S.: Assessment of land energy uptake in the industrial period from observational and model products, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-309, https://doi.org/10.5194/ems2022-309, 2022.

Reconstructions of the Last Millennium (LM) suggest coordinated changes in the hydroclimate of separate regions. This happens in regions of North America, North Africa and Europe for the periods of the Medieval Climate Anomaly (MCA; ca. 950–1250 CE) and the Little Ice Age (LIA; ca. 1450–1850 CE), but also in tropical areas, where consistent changes are found in regions of South America, East Africa, Southeast Asia and the Indo-Pacific basin. For extratropical areas, changes in the hydroclimate have been mostly linked to changes in variability modes like the Northern (NAM) and Southern Annular Modes (SAM), while in tropical areas they are mostly associated to alterations in the position and intensity of the Intertropical Convergence Zone (ITCZ).

To assess these large-scale changes in the hydroclimate of the LM and the mechanisms explaining them, climate simulations from the Community Earth System Model - Last Millennium Ensemble (CESM-LME) have been analysed. To assess the consistency of the results for different regions, these analyses have been also compared with the information provided by proxy-based datasets, including the Drought Atlases for Europe (OWDA), North America (NADA), Asia (MADA), Mexico (MXDA), Eastern Australia and New Zealand (ANZDA), the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR).

Simulations show that changes in the hydroclimate of extratropical and tropical regions of the Atlantic Ocean basin are mainly linked to changes in external forcing factors, while for the tropical areas of the Pacific and Indian Ocean basins, there is a significant contribution of internal variability. The understanding of the mechanisms behind these large-scale changes could help not only to better characterize the evolution of the hydroclimate of tropical and extratropical regions, but also the ability of the model simulations to reproduce the behavior of hydroclimate depending on the region.

How to cite: Roldán-Gómez, P. J., González-Rouco, J. F., Smerdon, J., and Melo-Aguilar, C.: Changes in tropical and extratropical hydroclimate during the Last Millennium, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-285, https://doi.org/10.5194/ems2022-285, 2022.

Several techniques are commonly used for quantitative climate reconstructions based on plant information from lake sediments. One of these is the creation of transfer functions that link current climate to current plant distributions and apply the results to pollen information from sediment cores. However, one challenge is to combine this information with that of other proxies. Therefore, we have developed a new algorithm that automatically creates a compromise from all available proxy information. This technique has already been successfully tested on over 500 sediment cores throughout Europe and the Middle East. Moreover, it allows us to reconstruct not only the climate of the last centuries, but also that of several glacial-interglacial cycles. In this context, the following proxies were used: Plant information from lake sediment cores, isotopic information from speleothems, marine cores, and ice cores.

To achieve this, we need to solve four major tasks. First, we conducted a machine learning competition to find the best generalized transfer functions linking current climate to vegetation data. Second, we developed a Bayesian-based age-depth/distance transformation that allows us to construct a regular temporal grid not only for sediment cores but also for speleothems. This computationally fast and easy-to-implement transformation allows us to include all age uncertainties in our reconstruction method. Third, if the proxies are to be considered in spectral space rather than temporal space, this can be achieved by our method using wavelet power spectra. Fourth, we have designed a fast algorithm that combines all inserted proxy information. This was made possible through the use of a Markov chain Monte Carlo method that derives specific weights for each taxon based on the included proxy information. In addition, this flexible technique can be enhanced by incorporating direct climate information, e.g., from instrumental meteorological measurements, from results of paleoclimate simulations, and/or from historical records. If the human influence on a proxy in a given period is not negligible, the algorithm ignores it and focuses on the remaining proxies to minimize this effect. Therefore, the procedure can be easily extended with further proxy information such as CO2 and/or solar insolation.

In summary, our new method provides quantitative paleoclimate reconstructions that approximate proxies not only in temporal space but also in spectral space, and can be further constrained by climate anchor points.

How to cite: Netzel, T., Hense, A., Litt, T., and Miebach, A.: Quantitative paleoclimate reconstructions based on multiple proxies in southeastern Europe, Turkey, and the Levant, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-634, https://doi.org/10.5194/ems2022-634, 2022.

The study analyzes the atmospheric variability that caused the largest floods affecting the town of Tortosa in the mouth of the Ebro River (northeast Iberian Peninsula). The Tortosa flood database and flood marks in the nearby town of Xerta are used to define the more relevant flooding episodes (discharges > 2900 m³s^-1) of the 1600-2005 period. We explore the atmospheric variability based on low-frequency patterns and synoptic types applying a multivariable analysis to grids at sea-level pressure and geopotential at 500 hPa provided by the 20th Century V3 Reanalysis Project for the instrumental period (since 1836). Output from the Last Millennium Ensemble Project and climate reconstructions (Luterbacher et al., 2002) were used to analyze the sea-level pressure over the pre-instrumental period (before 1836).

The synoptic analysis includes 33 flood episodes since 1836. Four synoptic types are related to floods in Tortosa, characterized by low-pressure systems that interact with the Mediterranean warm air-mass and promote the atmosphere destabilization. The prevailing synoptic conditions that favor heavy rainfall explains well the hydrological reconstruction of the flood events. The synoptic types are characterized by Atlantic low-pressure systems that interact with the Mediterranean warm air mass that destabilizes the atmosphere due to temperature differences between the surface (warm and moist air from the Mediterranean Sea) and the middle levels of the troposphere. Furthermore, due to  high-pressure systems located in Central Europe causing stagnation of the synoptic configuration, long-lasting rainfall can occur over the Ebro basin.

We detected four clusters of high-frequency flooding in Tortosa since 1600: 1617-1643, 1710-1787, 1825-1884, and 1907-1985. Most of these are related phases of high solar variability, which highlight atmospheric and hydrological instability in periods of rapid climate change. The low-frequency atmospheric variability connected to these flood periods is related to the positive phase of the NAO, relative high values of solar activity and positive Northern Hemisphere temperature anomalies. This provides evidence that complex solar processes might provoke changes in temperature and variability in the atmospheric circulation. The NAO shows that the major floods in the region are related to the zonal atmospheric circulation. These atmospheric disturbances have a winter effect in the western part of the basin, while the Pyrenean sub-basins are affected during autumn.

The major finding is that similar flood behavior is detected since 1600. Results from our study help to improve our understanding of the past, present and future climates, as well as their impacts, thereby enhancing the knowledge base for addressing some aspects and impacts of climate change in order to reduce uncertainty about future outcomes. Furthermore, future investigations seeking to detect and prevent extreme events will find it particularly useful to establish relationships between modes of low-frequency atmospheric variability, synoptic types and flooding.

How to cite: Peña, J. C., Balasch, J. C., Pino, D., Schulte, L., Barriendos, M., Ruiz-Bellet, J. L., Prohom, M., Tuset, J., Mazon, J., and Castelltort, X.: Atmospheric variability linked to large floods in the lower Ebro River basin, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-230, https://doi.org/10.5194/ems2022-230, 2022.

There is a growing need for past weather and climate data to support science and decision-making. This presentation describes the compilation and the construction of global monthly instrumental climate data with a focus on the 18th and early 19th centuries. This database provides early instrumental data, and it is multivariable (air temperature, pressure, precipitation sum, number of precipitation days) are recovered for thousands of locations around the world for that encompasses a substantial body of the known early instrumental time series. Instrumental meteorological measurements from periods prior to the start of national weather services are designated “early instrumental data”. Much of the data is taken from repositories we know (GHCN, ISTI, CRUTEM, Berkeley Earth, HISTALP). In addition, many of these stations have not been digitized before. It ends up in one database with the same format, so it is easy to use the data with a good overview of metadata. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. The first record is from 1586. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate data set for the preindustrial period.

How to cite: Lundstad, E.: Global Historical Climate Database - HCLIM, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-544, https://doi.org/10.5194/ems2022-544, 2022.